Pan-Cancer, Genome-Scale Metabolic Network Analysis of over 10,000 Patients Elucidates Relationship between Metabolism and Survival.

Despite the high variability in cancer biology, cancers nevertheless exhibit cohesive hallmarks across multiple cancer types, notably dysregulated metabolism. Metabolism plays a central role in cancer biology, and shifts in metabolic pathways have been linked to tumor aggressiveness and likelihood of response to therapy. We therefore sought to interrogate metabolism across cancer types and understand how intrinsic modes of metabolism vary within and across indications and how they relate to patient prognosis. We used context specific genome-scale metabolic modeling to simulate metabolism across 10,915 patients from 34 cancer types from The Cancer Genome Atlas and the MMRF-COMMPASS study. We found that cancer metabolism clustered into modes characterized by differential glycolysis, oxidative phosphorylation, and growth rate. We also found that the simulated activities of metabolic pathways are intrinsically prognostic across cancer types, especially tumor growth rate, fatty acid biosynthesis, folate metabolism, oxidative phosphorylation, steroid metabolism, and glutathione metabolism. This work shows the prognostic power of individual patient metabolic modeling across multiple cancer types. Additionally, it shows that analyzing large-scale models of cancer metabolism with survival information provides unique insights into underlying relationships across cancer types and suggests how therapies designed for one cancer type may be repurposed for use in others.

Next generation immuno-oncology tumor profiling using a rapid, non-invasive, computational biophysics biomarker in early-stage breast cancer.

Background: Immuno-oncology (IO) therapies targeting the PD-1/PD-L1 axis, such as immune checkpoint inhibitor (ICI) antibodies, have emerged as promising treatments for early-stage breast cancer (ESBC). Despite immunotherapy's clinical significance, the number of benefiting patients remains small, and the therapy can prompt severe immune-related events. Current pathologic and transcriptomic predictions of IO response are limited in terms of accuracy and rely on single-site biopsies, which cannot fully account for tumor heterogeneity. In addition, transcriptomic analyses are costly and time-consuming. We therefore constructed a computational biomarker coupling biophysical simulations and artificial intelligence-based tissue segmentation of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRIs), enabling IO response prediction across the entire tumor. Methods: By analyzing both single-cell and whole-tissue RNA-seq data from non-IO-treated ESBC patients, we associated gene expression levels of the PD-1/PD-L1 axis with local tumor biology. PD-L1 expression was then linked to biophysical features derived from DCE-MRIs to generate spatially- and temporally-resolved atlases (virtual tumors) of tumor biology, as well as the TumorIO biomarker of IO response. We quantified TumorIO within patient virtual tumors (n = 63) using integrative modeling to train and develop a corresponding TumorIO Score. Results: We validated the TumorIO biomarker and TumorIO Score in a small, independent cohort of IO-treated patients (n = 17) and correctly predicted pathologic complete response (pCR) in 15/17 individuals (88.2% accuracy), comprising 10/12 in triple negative breast cancer (TNBC) and 5/5 in HR+/HER2- tumors. We applied the TumorIO Score in a virtual clinical trial (n = 292) simulating ICI administration in an IO-naïve cohort that underwent standard chemotherapy. Using this approach, we predicted pCR rates of 67.1% for TNBC and 17.9% for HR+/HER2- tumors with addition of IO therapy; comparing favorably to empiric pCR rates derived from published trials utilizing ICI in both cancer subtypes. Conclusion: The TumorIO biomarker and TumorIO Score represent a next generation approach using integrative biophysical analysis to assess cancer responsiveness to immunotherapy. This computational biomarker performs as well as PD-L1 transcript levels in identifying a patient's likelihood of pCR following anti-PD-1 IO therapy. The TumorIO biomarker allows for rapid IO profiling of tumors and may confer high clinical decision impact to further enable personalized oncologic care.

Novel computational biology modeling system can accurately forecast response to neoadjuvant therapy in early breast cancer.

BACKGROUND: Generalizable population-based studies are unable to account for individual tumor heterogeneity that contributes to variability in a patient's response to physician-chosen therapy. Although molecular characterization of tumors has advanced precision medicine, in early-stage and locally advanced breast cancer patients, predicting a patient's response to neoadjuvant therapy (NAT) remains a gap in current clinical practice. Here, we perform a study in an independent cohort of early-stage and locally advanced breast cancer patients to forecast tumor response to NAT and assess the stability of a previously validated biophysical simulation platform. METHODS: A single-blinded study was performed using a retrospective database from a single institution (9/2014-12/2020). Patients included: ≥ 18 years with breast cancer who completed NAT, with pre-treatment dynamic contrast enhanced magnetic resonance imaging. Demographics, chemotherapy, baseline (pre-treatment) MRI and pathologic data were input into the TumorScope Predict (TS) biophysical simulation platform to generate predictions. Primary outcomes included predictions of pathological complete response (pCR) versus residual disease (RD) and final volume for each tumor. For validation, post-NAT predicted pCR and tumor volumes were compared to actual pathological assessment and MRI-assessed volumes. Predicted pCR was pre-defined as residual tumor volume ≤ 0.01 cm3 (≥ 99.9% reduction). RESULTS: The cohort consisted of eighty patients; 36 Caucasian and 40 African American. Most tumors were high-grade (54.4% grade 3) invasive ductal carcinomas (90.0%). Receptor subtypes included hormone receptor positive (HR+)/human epidermal growth factor receptor 2 positive (HER2+, 30%), HR+/HER2- (35%), HR-/HER2+ (12.5%) and triple negative breast cancer (TNBC, 22.5%). Simulated tumor volume was significantly correlated with post-treatment radiographic MRI calculated volumes (r = 0.53, p = 1.3 × 10-7, mean absolute error of 6.57%). TS prediction of pCR compared favorably to pathological assessment (pCR: TS n = 28; Path n = 27; RD: TS n = 52; Path n = 53), for an overall accuracy of 91.2% (95% CI: 82.8% - 96.4%; Clopper-Pearson interval). Five-year risk of recurrence demonstrated similar prognostic performance between TS predictions (Hazard ratio (HR): - 1.99; 95% CI [- 3.96, - 0.02]; p = 0.043) and clinically assessed pCR (HR: - 1.76; 95% CI [- 3.75, 0.23]; p = 0.054). CONCLUSION: We demonstrated TS ability to simulate and model tumor in vivo conditions in silico and forecast volume response to NAT across breast tumor subtypes.

Highly accurate response prediction in high-risk early breast cancer patients using a biophysical simulation platform.

PURPOSE: Pathologic complete response (pCR) to neoadjuvant chemotherapy (NAC) in early breast cancer (EBC) is largely dependent on breast cancer subtype, but no clinical-grade model exists to predict response and guide selection of treatment. A biophysical simulation of response to NAC has the potential to address this unmet need. METHODS: We conducted a retrospective evaluation of a biophysical simulation model as a predictor of pCR. Patients who received standard NAC at the University of Chicago for EBC between January 1st, 2010 and March 31st, 2020 were included. Response was predicted using baseline breast MRI, clinicopathologic features, and treatment regimen by investigators who were blinded to patient outcomes. RESULTS: A total of 144 tumors from 141 patients were included; 59 were triple-negative, 49 HER2-positive, and 36 hormone-receptor positive/HER2 negative. Lymph node disease was present in half of patients, and most were treated with an anthracycline-based regimen (58.3%). Sensitivity and specificity of the biophysical simulation for pCR were 88.0% (95% confidence interval [CI] 75.7 - 95.5) and 89.4% (95% CI 81.3 - 94.8), respectively, with robust results regardless of subtype. In patients with predicted pCR, 5-year event-free survival was 98%, versus 79% with predicted residual disease (log-rank p = 0.01, HR 4.57, 95% CI 1.36 - 15.34). At a median follow-up of 5.4 years, no patients with predicted pCR experienced disease recurrence. CONCLUSION: A biophysical simulation model accurately predicts pCR and long-term outcomes from baseline MRI and clinical data, and is a promising tool to guide escalation/de-escalation of NAC.

Imaging the Landmarks of Vascular Recovery.

Background: Peripheral arterial disease (PAD) is a major worldwide health concern. Since the late 1990s therapeutic angiogenesis has been investigated as an alternative to traditional PAD treatments. Although positive preclinical results abound in the literature, the outcomes of human clinical trials have been discouraging. Among the challenges the field has faced has been a lack of standardization of the timings and measures used to validate new treatment approaches. Methods: In order to study the spatiotemporal dynamics of both perfusion and neovascularization in mice subjected to surgically-induced hindlimb ischemia (n= 30), we employed three label-free imaging modalities (a novel high-sensitivity ultrasonic Power Doppler methodology, laser speckle contrast, and photoacoustic imaging), as well as a tandem of radio-labeled molecular probes, 99mTc-NC100692 and 99mTc-BRU-5921 respectively, designed to detect two key modulators of angiogenic activity, αVß3 and HIF-1α , via scintigraphic imaging. Results: The multimodal imaging strategy reveals a set of "landmarks"-key physiological and molecular events in the healing process-that can serve as a standardized framework for describing the impact of emerging PAD treatments. These landmarks span the entire process of neovascularization, beginning with the rapid decreases in perfusion and oxygenation associated with ligation surgery, extending through pro-angiogenic changes in gene expression driven by the master regulator HIF-1α , and ultimately leading to complete functional revascularization of the affected tissues. Conclusions: This study represents an important step in the development of multimodal non-invasive imaging strategies for vascular research; the combined results offer more insight than can be gleaned through any of the individual imaging methods alone. Researchers adopting similar imaging strategies and will be better able to describe changes in the onset, duration, and strength of each of the landmarks of vascular recovery, yielding greater biological insight, and enabling more comprehensive cross-study comparisons. Perhaps most important, this study paves the road for more efficient translation of PAD research; emerging experimental treatments can be more effectively assessed and refined at the preclinical stage, ultimately leading to better next-generation therapies.

Multimodal Assessment of Mesenchymal Stem Cell Therapy for Diabetic Vascular Complications.

Peripheral arterial disease (PAD) is a debilitating complication of diabetes mellitus (DM) that leads to thousands of injuries, amputations, and deaths each year. The use of mesenchymal stem cells (MSCs) as a regenerative therapy holds the promise of regrowing injured vasculature, helping DM patients live healthier and longer lives. We report the use of muscle-derived MSCs to treat surgically-induced hindlimb ischemia in a mouse model of type 1 diabetes (DM-1). We serially evaluate several facets of the recovery process, including αVß3 -integrin expression (a marker of angiogenesis), blood perfusion, and muscle function. We also perform microarray transcriptomics experiments to characterize the gene expression states of the MSC-treated is- chemic tissues, and compare the results with those of non-ischemic tissues, as well as ischemic tissues from a saline-treated control group. The results show a multifaceted impact of mMSCs on hindlimb ischemia. We determined that the angiogenic activity one week after mMSC treatment was enhanced by approximately 80% relative to the saline group, which resulted in relative increases in blood perfusion and muscle strength of approximately 42% and 1.7-fold, respectively. At the transcriptomics level, we found that several classes of genes were affected by mMSC treatment. The mMSCs appeared to enhance both pro-angiogenic and metabolic genes, while suppressing anti-angiogenic genes and certain genes involved in the inflammatory response. All told, mMSC treatment appears to exert far-reaching effects on the microenvironment of ischemic tissue, enabling faster and more complete recovery from vascular occlusion.

Synthesis, Chemical Characterization and Multiscale Biological Evaluation of a Dimeric-cRGD Peptide for Targeted Imaging of α V ß 3 Integrin Activity.

Cyclic peptides containing the Arg-Gly-Asp (RGD) sequence have been shown to specifically bind the angiogenesis biomarker α V ß 3 integrin. We report the synthesis, chemical characterization, and biological evaluation of two novel dimeric cyclic RGD-based molecular probes for the targeted imaging of α V ß 3 activity (a radiolabeled version, 64Cu-NOTA-PEG4-cRGD2, for PET imaging, and a fluorescent version, FITC-PEG4-cRGD2, for in vitro work). We investigated the performance of this probe at the receptor, cell, organ, and whole-body levels, including its use to detect diabetes associated impairment of ischemia-induced myocardial angiogenesis. Both versions of the probe were found to be stable, demonstrated fast receptor association constants, and showed high specificity for α V ß 3 in HUVECs (K d ~ 35 nM). Dynamic PET-CT imaging indicated rapid blood clearance via kidney filtration, and accumulation within α V ß 3-positive infarcted myocardium. 64Cu-NOTA-PEG4-cRGD2 demonstrated a favorable biodistribution, slow washout, and excellent performance with respect to the quality of the PET-CT images obtained. Importantly, the ratio of probe uptake in infarcted heart tissue compared to normal tissue was significantly higher in non-diabetic rats than in diabetic ones. Overall, our probes are promising agents for non-invasive quantitative imaging of α V ß 3 expression, both in vitro and in vivo.

Spatially-resolved metabolic cooperativity within dense bacterial colonies.

BACKGROUND: The exchange of metabolites and the reprogramming of metabolism in response to shifting microenvironmental conditions can drive subpopulations of cells within colonies toward divergent behaviors. Understanding the interactions of these subpopulations-their potential for competition as well as cooperation-requires both a metabolic model capable of accounting for a wide range of environmental conditions, and a detailed dynamic description of the cells' shared extracellular space. RESULTS: Here we show that a cell's position within an in silico Escherichia coli colony grown on glucose minimal agar can drastically affect its metabolism: "pioneer" cells at the outer edge engage in rapid growth that expands the colony, while dormant cells in the interior separate two spatially distinct subpopulations linked by a cooperative form of acetate crossfeeding that has so far gone unnoticed. Our hybrid simulation technique integrates 3D reaction-diffusion modeling with genome-scale flux balance analysis (FBA) to describe the position-dependent metabolism and growth of cells within a colony. Our results are supported by imaging experiments involving strains of fluorescently-labeled E. coli. The spatial patterns of fluorescence within these experimental colonies identify cells with upregulated genes associated with acetate crossfeeding and are in excellent agreement with the predictions. Furthermore, the height-to-width ratios of both the experimental and simulated colonies are in good agreement over a growth period of 48 hours. CONCLUSIONS: Our modeling paradigm can accurately reproduce a number of known features of E. coli colony growth, as well as predict a novel one that had until now gone unrecognized. The acetate crossfeeding we see has a direct analogue in a form of lactate crossfeeding observed in certain forms of cancer, and we anticipate future application of our methodology to models of tissues and tumors.